Abstract:This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic problem and three UCI ML repository datasets. In most cases, we outperform manifold regularization of support vector machines, which is a state-of-the-art approach to semi-supervised max-margin learning.
| Comments: | Published at AISTATS 2010 (13th International Conference on Artificial Intelligence and Statistics) |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.26818 [cs.LG] |
| (or arXiv:2604.26818v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26818 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Michal Valko [view email]
[v1]
Wed, 29 Apr 2026 15:46:46 UTC (633 KB)